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Related Experiment Video

Updated: Jul 7, 2026

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Construction of optimal subband coders using optimized and optimal quantizers.

Michael Gerassimos Strintzis¹, Nikolaos V Boulgouris

¹Dept. of Electr. and Comput. Eng., Aristotle Univ. of Thessaloniki, Greece. strintzi@eng.auth.gr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 5, 2008

Summary

This summary is machine-generated.

This study introduces optimized quantizers and filter banks for subband coding, minimizing error variance. A new bit allocation method improves image coding performance compared to wavelet methods.

Related Experiment Videos

Last Updated: Jul 7, 2026

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Area of Science:

Digital Signal Processing
Image Compression
Information Theory

Background:

Quantizers are essential components in digital signal processing and data compression.
Optimizing quantizers is crucial for minimizing distortion and improving coding efficiency.
Subband coding offers a flexible framework for signal decomposition and reconstruction.

Purpose of the Study:

To present a method for optimizing arbitrary quantizers using a compensating postfilter.
To derive an expression for the error variance in subband coders with optimized quantizers.
To develop a novel method for optimal bit allocation in filter banks with optimized quantizers.

Main Methods:

Optimization of quantizers using a compensating postfilter.
Modeling optimized quantizers as linear time-invariant filters with additive noise.
Derivation of error variance expression for subband coders.
Minimization of error variance through optimal synthesis and analysis filter selection.
Development of a new bit allocation strategy for subbands.

Main Results:

Optimized quantizers are shown to fit a specific model, similar to optimal (Lloyd-Max) quantizers.
An explicit expression for error variance in subband coders with optimized quantizers is determined.
Error variance is minimized by selecting synthesis filters that achieve perfect reconstruction.
Globally optimum filter banks are obtained by optimizing analysis filters.
Experimental comparison shows the proposed scheme outperforms classical wavelet coding methods.

Conclusions:

The proposed method effectively optimizes quantizers and filter banks for subband coding.
The developed bit allocation strategy enhances coding efficiency.
The optimized subband image coding scheme demonstrates superior performance over existing wavelet-based methods.